Overview

Brought to you by YData

Dataset statistics

Number of variables42
Number of observations154277
Missing cells0
Missing cells (%)0.0%
Duplicate rows6427
Duplicate rows (%)4.2%
Total size in memory50.6 MiB
Average record size in memory344.0 B

Variable types

Numeric26
Categorical15
Text1

Alerts

urgent has constant value "0"Constant
num_outbound_cmds has constant value "0"Constant
is_host_login has constant value "0"Constant
Dataset has 6427 (4.2%) duplicate rowsDuplicates
count is highly overall correlated with dst_bytes and 8 other fieldsHigh correlation
diff_srv_rate is highly overall correlated with dst_host_diff_srv_rate and 9 other fieldsHigh correlation
dst_bytes is highly overall correlated with count and 2 other fieldsHigh correlation
dst_host_count is highly overall correlated with count and 3 other fieldsHigh correlation
dst_host_diff_srv_rate is highly overall correlated with count and 11 other fieldsHigh correlation
dst_host_rerror_rate is highly overall correlated with dst_host_srv_rerror_rate and 2 other fieldsHigh correlation
dst_host_same_src_port_rate is highly overall correlated with count and 12 other fieldsHigh correlation
dst_host_same_srv_rate is highly overall correlated with diff_srv_rate and 11 other fieldsHigh correlation
dst_host_serror_rate is highly overall correlated with diff_srv_rate and 9 other fieldsHigh correlation
dst_host_srv_count is highly overall correlated with count and 12 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly overall correlated with dst_bytes and 1 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly overall correlated with dst_host_rerror_rate and 2 other fieldsHigh correlation
dst_host_srv_serror_rate is highly overall correlated with diff_srv_rate and 9 other fieldsHigh correlation
hot is highly overall correlated with is_guest_login and 1 other fieldsHigh correlation
is_guest_login is highly overall correlated with hotHigh correlation
label is highly overall correlated with land and 5 other fieldsHigh correlation
land is highly overall correlated with labelHigh correlation
logged_in is highly overall correlated with count and 3 other fieldsHigh correlation
num_access_files is highly overall correlated with num_compromised and 2 other fieldsHigh correlation
num_compromised is highly overall correlated with hot and 2 other fieldsHigh correlation
num_failed_logins is highly overall correlated with labelHigh correlation
num_root is highly overall correlated with num_access_files and 1 other fieldsHigh correlation
protocol_type is highly overall correlated with count and 6 other fieldsHigh correlation
rerror_rate is highly overall correlated with dst_host_rerror_rate and 2 other fieldsHigh correlation
root_shell is highly overall correlated with labelHigh correlation
same_srv_rate is highly overall correlated with diff_srv_rate and 9 other fieldsHigh correlation
serror_rate is highly overall correlated with diff_srv_rate and 9 other fieldsHigh correlation
src_bytes is highly overall correlated with count and 11 other fieldsHigh correlation
srv_count is highly overall correlated with count and 6 other fieldsHigh correlation
srv_rerror_rate is highly overall correlated with dst_host_rerror_rate and 2 other fieldsHigh correlation
srv_serror_rate is highly overall correlated with diff_srv_rate and 9 other fieldsHigh correlation
su_attempted is highly overall correlated with num_access_files and 2 other fieldsHigh correlation
wrong_fragment is highly overall correlated with labelHigh correlation
flag is highly imbalanced (69.9%)Imbalance
land is highly imbalanced (99.8%)Imbalance
wrong_fragment is highly imbalanced (95.4%)Imbalance
num_failed_logins is highly imbalanced (99.7%)Imbalance
root_shell is highly imbalanced (99.8%)Imbalance
su_attempted is highly imbalanced (> 99.9%)Imbalance
num_shells is highly imbalanced (99.9%)Imbalance
num_access_files is highly imbalanced (99.6%)Imbalance
is_guest_login is highly imbalanced (97.3%)Imbalance
label is highly imbalanced (54.0%)Imbalance
duration is highly skewed (γ1 = 26.10314625)Skewed
src_bytes is highly skewed (γ1 = 390.8421675)Skewed
dst_bytes is highly skewed (γ1 = 94.56909049)Skewed
hot is highly skewed (γ1 = 20.42565009)Skewed
num_compromised is highly skewed (γ1 = 349.3468716)Skewed
num_root is highly skewed (γ1 = 355.2700399)Skewed
num_file_creations is highly skewed (γ1 = 170.9496904)Skewed
duration has 150157 (97.3%) zerosZeros
src_bytes has 36324 (23.5%) zerosZeros
dst_bytes has 126570 (82.0%) zerosZeros
hot has 151480 (98.2%) zerosZeros
num_compromised has 152106 (98.6%) zerosZeros
num_root has 154100 (99.9%) zerosZeros
num_file_creations has 154194 (99.9%) zerosZeros
serror_rate has 126408 (81.9%) zerosZeros
srv_serror_rate has 127642 (82.7%) zerosZeros
rerror_rate has 143479 (93.0%) zerosZeros
srv_rerror_rate has 143204 (92.8%) zerosZeros
same_srv_rate has 2050 (1.3%) zerosZeros
diff_srv_rate has 119317 (77.3%) zerosZeros
srv_diff_host_rate has 142812 (92.6%) zerosZeros
dst_host_same_srv_rate has 5104 (3.3%) zerosZeros
dst_host_diff_srv_rate has 107167 (69.5%) zerosZeros
dst_host_same_src_port_rate has 45251 (29.3%) zerosZeros
dst_host_srv_diff_host_rate has 137157 (88.9%) zerosZeros
dst_host_serror_rate has 124366 (80.6%) zerosZeros
dst_host_srv_serror_rate has 125858 (81.6%) zerosZeros
dst_host_rerror_rate has 139900 (90.7%) zerosZeros
dst_host_srv_rerror_rate has 140793 (91.3%) zerosZeros

Reproduction

Analysis started2024-09-29 10:26:00.970929
Analysis finished2024-09-29 10:27:48.960292
Duration1 minute and 47.99 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

duration
Real number (ℝ)

SKEWED  ZEROS 

Distinct1073
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.705452
Minimum0
Maximum42448
Zeros150157
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:49.151324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum42448
Range42448
Interquartile range (IQR)0

Descriptive statistics

Standard deviation864.60825
Coefficient of variation (CV)15.247357
Kurtosis881.26108
Mean56.705452
Median Absolute Deviation (MAD)0
Skewness26.103146
Sum8748347
Variance747547.42
MonotonicityNot monotonic
2024-09-29T12:27:49.322010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 150157
97.3%
1 881
 
0.6%
2 314
 
0.2%
3 195
 
0.1%
5 174
 
0.1%
4 165
 
0.1%
2630 144
 
0.1%
14 98
 
0.1%
12 55
 
< 0.1%
7 53
 
< 0.1%
Other values (1063) 2041
 
1.3%
ValueCountFrequency (%)
0 150157
97.3%
1 881
 
0.6%
2 314
 
0.2%
3 195
 
0.1%
4 165
 
0.1%
5 174
 
0.1%
6 52
 
< 0.1%
7 53
 
< 0.1%
8 35
 
< 0.1%
9 47
 
< 0.1%
ValueCountFrequency (%)
42448 1
< 0.1%
42088 1
< 0.1%
41065 1
< 0.1%
40929 1
< 0.1%
40806 1
< 0.1%
40682 1
< 0.1%
40571 1
< 0.1%
40448 1
< 0.1%
40339 1
< 0.1%
40232 1
< 0.1%

protocol_type
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
icmp
86066 
tcp
61301 
udp
 
6910

Length

Max length4
Median length4
Mean length3.5578667
Min length3

Characters and Unicode

Total characters548897
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowicmp
2nd rowicmp
3rd rowtcp
4th rowtcp
5th rowicmp

Common Values

ValueCountFrequency (%)
icmp 86066
55.8%
tcp 61301
39.7%
udp 6910
 
4.5%

Length

2024-09-29T12:27:49.476699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:49.625276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
icmp 86066
55.8%
tcp 61301
39.7%
udp 6910
 
4.5%

Most occurring characters

ValueCountFrequency (%)
p 154277
28.1%
c 147367
26.8%
i 86066
15.7%
m 86066
15.7%
t 61301
 
11.2%
u 6910
 
1.3%
d 6910
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 548897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 154277
28.1%
c 147367
26.8%
i 86066
15.7%
m 86066
15.7%
t 61301
 
11.2%
u 6910
 
1.3%
d 6910
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 548897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 154277
28.1%
c 147367
26.8%
i 86066
15.7%
m 86066
15.7%
t 61301
 
11.2%
u 6910
 
1.3%
d 6910
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 548897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 154277
28.1%
c 147367
26.8%
i 86066
15.7%
m 86066
15.7%
t 61301
 
11.2%
u 6910
 
1.3%
d 6910
 
1.3%
Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:49.893792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length5
Mean length5.3719284
Min length3

Characters and Unicode

Total characters828765
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowecr_i
2nd rowecr_i
3rd rowprivate
4th rowprivate
5th rowecr_i
ValueCountFrequency (%)
ecr_i 84517
54.8%
private 34784
22.5%
http 20927
 
13.6%
other 3201
 
2.1%
smtp 2921
 
1.9%
ftp_data 1982
 
1.3%
domain_u 1719
 
1.1%
eco_i 1382
 
0.9%
ftp 461
 
0.3%
finger 235
 
0.2%
Other values (55) 2148
 
1.4%
2024-09-29T12:27:50.311536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 125306
15.1%
i 123333
14.9%
r 123225
14.9%
_ 90295
10.9%
t 88615
10.7%
c 86210
10.4%
p 62063
7.5%
a 40927
 
4.9%
v 34817
 
4.2%
h 24529
 
3.0%
Other values (28) 29445
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 828765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 125306
15.1%
i 123333
14.9%
r 123225
14.9%
_ 90295
10.9%
t 88615
10.7%
c 86210
10.4%
p 62063
7.5%
a 40927
 
4.9%
v 34817
 
4.2%
h 24529
 
3.0%
Other values (28) 29445
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 828765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 125306
15.1%
i 123333
14.9%
r 123225
14.9%
_ 90295
10.9%
t 88615
10.7%
c 86210
10.4%
p 62063
7.5%
a 40927
 
4.9%
v 34817
 
4.2%
h 24529
 
3.0%
Other values (28) 29445
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 828765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 125306
15.1%
i 123333
14.9%
r 123225
14.9%
_ 90295
10.9%
t 88615
10.7%
c 86210
10.4%
p 62063
7.5%
a 40927
 
4.9%
v 34817
 
4.2%
h 24529
 
3.0%
Other values (28) 29445
 
3.6%

flag
Categorical

IMBALANCE 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
SF
117731 
S0
26135 
REJ
 
9136
RSTR
 
878
RSTO
 
233
Other values (6)
 
164

Length

Max length6
Median length2
Mean length2.0739579
Min length2

Characters and Unicode

Total characters319964
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSF
2nd rowSF
3rd rowS0
4th rowREJ
5th rowSF

Common Values

ValueCountFrequency (%)
SF 117731
76.3%
S0 26135
 
16.9%
REJ 9136
 
5.9%
RSTR 878
 
0.6%
RSTO 233
 
0.2%
SH 103
 
0.1%
S1 25
 
< 0.1%
S2 12
 
< 0.1%
RSTOS0 11
 
< 0.1%
OTH 8
 
< 0.1%

Length

2024-09-29T12:27:50.494251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf 117731
76.3%
s0 26135
 
16.9%
rej 9136
 
5.9%
rstr 878
 
0.6%
rsto 233
 
0.2%
sh 103
 
0.1%
s1 25
 
< 0.1%
s2 12
 
< 0.1%
rstos0 11
 
< 0.1%
oth 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 145144
45.4%
F 117731
36.8%
0 26146
 
8.2%
R 11136
 
3.5%
E 9136
 
2.9%
J 9136
 
2.9%
T 1130
 
0.4%
O 252
 
0.1%
H 111
 
< 0.1%
1 25
 
< 0.1%
Other values (2) 17
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 319964
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 145144
45.4%
F 117731
36.8%
0 26146
 
8.2%
R 11136
 
3.5%
E 9136
 
2.9%
J 9136
 
2.9%
T 1130
 
0.4%
O 252
 
0.1%
H 111
 
< 0.1%
1 25
 
< 0.1%
Other values (2) 17
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 319964
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 145144
45.4%
F 117731
36.8%
0 26146
 
8.2%
R 11136
 
3.5%
E 9136
 
2.9%
J 9136
 
2.9%
T 1130
 
0.4%
O 252
 
0.1%
H 111
 
< 0.1%
1 25
 
< 0.1%
Other values (2) 17
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 319964
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 145144
45.4%
F 117731
36.8%
0 26146
 
8.2%
R 11136
 
3.5%
E 9136
 
2.9%
J 9136
 
2.9%
T 1130
 
0.4%
O 252
 
0.1%
H 111
 
< 0.1%
1 25
 
< 0.1%
Other values (2) 17
 
< 0.1%

src_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2150
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7973.2607
Minimum0
Maximum6.9337564 × 108
Zeros36324
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:50.658757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128
median520
Q31032
95-th percentile1032
Maximum6.9337564 × 108
Range6.9337564 × 108
Interquartile range (IQR)1004

Descriptive statistics

Standard deviation1768217.5
Coefficient of variation (CV)221.76843
Kurtosis153257.48
Mean7973.2607
Median Absolute Deviation (MAD)512
Skewness390.84217
Sum1.2300907 × 109
Variance3.1265932 × 1012
MonotonicityNot monotonic
2024-09-29T12:27:50.842559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1032 68245
44.2%
0 36324
23.5%
520 15921
 
10.3%
105 2257
 
1.5%
54540 2143
 
1.4%
28 980
 
0.6%
8 961
 
0.6%
147 813
 
0.5%
334 617
 
0.4%
146 577
 
0.4%
Other values (2140) 25439
 
16.5%
ValueCountFrequency (%)
0 36324
23.5%
1 257
 
0.2%
4 1
 
< 0.1%
5 8
 
< 0.1%
6 36
 
< 0.1%
7 30
 
< 0.1%
8 961
 
0.6%
9 44
 
< 0.1%
10 55
 
< 0.1%
11 4
 
< 0.1%
ValueCountFrequency (%)
693375640 1
 
< 0.1%
5135678 21
< 0.1%
5133877 1
 
< 0.1%
5133876 30
< 0.1%
5131424 7
 
< 0.1%
2500058 1
 
< 0.1%
2194619 6
 
< 0.1%
501760 3
 
< 0.1%
456980 1
 
< 0.1%
250736 1
 
< 0.1%

dst_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6155
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1242.1889
Minimum0
Maximum5155468
Zeros126570
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:51.025319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3082.2
Maximum5155468
Range5155468
Interquartile range (IQR)0

Descriptive statistics

Standard deviation52261.453
Coefficient of variation (CV)42.072065
Kurtosis9219.4713
Mean1242.1889
Median Absolute Deviation (MAD)0
Skewness94.56909
Sum1.9164118 × 108
Variance2.7312594 × 109
MonotonicityNot monotonic
2024-09-29T12:27:51.208267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 126570
82.0%
8314 2133
 
1.4%
105 1302
 
0.8%
146 750
 
0.5%
147 737
 
0.5%
145 308
 
0.2%
330 262
 
0.2%
42 257
 
0.2%
332 244
 
0.2%
331 241
 
0.2%
Other values (6145) 21473
 
13.9%
ValueCountFrequency (%)
0 126570
82.0%
1 2
 
< 0.1%
4 40
 
< 0.1%
6 1
 
< 0.1%
15 2
 
< 0.1%
17 3
 
< 0.1%
24 9
 
< 0.1%
26 2
 
< 0.1%
27 1
 
< 0.1%
28 3
 
< 0.1%
ValueCountFrequency (%)
5155468 1
< 0.1%
5153771 1
< 0.1%
5153460 1
< 0.1%
5151385 1
< 0.1%
5151154 1
< 0.1%
5151049 1
< 0.1%
5150938 1
< 0.1%
5150877 1
< 0.1%
5150841 1
< 0.1%
5150836 1
< 0.1%

land
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
154256 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154277
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154256
> 99.9%
1 21
 
< 0.1%

Length

2024-09-29T12:27:51.368650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:51.491287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 154256
> 99.9%
1 21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 154256
> 99.9%
1 21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154256
> 99.9%
1 21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154256
> 99.9%
1 21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154256
> 99.9%
1 21
 
< 0.1%

wrong_fragment
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
153039 
3
 
970
1
 
268

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154277
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 153039
99.2%
3 970
 
0.6%
1 268
 
0.2%

Length

2024-09-29T12:27:51.616693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:51.995910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 153039
99.2%
3 970
 
0.6%
1 268
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 153039
99.2%
3 970
 
0.6%
1 268
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 153039
99.2%
3 970
 
0.6%
1 268
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 153039
99.2%
3 970
 
0.6%
1 268
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 153039
99.2%
3 970
 
0.6%
1 268
 
0.2%

urgent
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
154277 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154277
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154277
100.0%

Length

2024-09-29T12:27:52.128486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:52.246891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 154277
100.0%

Most occurring characters

ValueCountFrequency (%)
0 154277
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154277
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154277
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154277
100.0%

hot
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.091147741
Minimum0
Maximum30
Zeros151480
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:52.354739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2813931
Coefficient of variation (CV)14.058418
Kurtosis435.00029
Mean0.091147741
Median Absolute Deviation (MAD)0
Skewness20.42565
Sum14062
Variance1.6419682
MonotonicityNot monotonic
2024-09-29T12:27:52.497955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 151480
98.2%
2 2151
 
1.4%
28 274
 
0.2%
1 165
 
0.1%
4 47
 
< 0.1%
6 37
 
< 0.1%
3 34
 
< 0.1%
5 25
 
< 0.1%
14 13
 
< 0.1%
30 11
 
< 0.1%
Other values (10) 40
 
< 0.1%
ValueCountFrequency (%)
0 151480
98.2%
1 165
 
0.1%
2 2151
 
1.4%
3 34
 
< 0.1%
4 47
 
< 0.1%
5 25
 
< 0.1%
6 37
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
30 11
 
< 0.1%
28 274
0.2%
24 5
 
< 0.1%
22 7
 
< 0.1%
20 10
 
< 0.1%
19 11
 
< 0.1%
18 2
 
< 0.1%
17 1
 
< 0.1%
14 13
 
< 0.1%
12 1
 
< 0.1%

num_failed_logins
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
154225 
1
 
51
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154277
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154225
> 99.9%
1 51
 
< 0.1%
5 1
 
< 0.1%

Length

2024-09-29T12:27:52.647055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:52.769327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 154225
> 99.9%
1 51
 
< 0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 154225
> 99.9%
1 51
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154225
> 99.9%
1 51
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154225
> 99.9%
1 51
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154225
> 99.9%
1 51
 
< 0.1%
5 1
 
< 0.1%

logged_in
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
129860 
1
24417 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154277
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 129860
84.2%
1 24417
 
15.8%

Length

2024-09-29T12:27:52.896806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:53.018647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 129860
84.2%
1 24417
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 129860
84.2%
1 24417
 
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 129860
84.2%
1 24417
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 129860
84.2%
1 24417
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 129860
84.2%
1 24417
 
15.8%

num_compromised
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.022044764
Minimum0
Maximum884
Zeros152106
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:53.130329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum884
Range884
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3650214
Coefficient of variation (CV)107.28269
Kurtosis127803.98
Mean0.022044764
Median Absolute Deviation (MAD)0
Skewness349.34687
Sum3401
Variance5.5933264
MonotonicityNot monotonic
2024-09-29T12:27:53.254267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 152106
98.6%
1 2141
 
1.4%
2 13
 
< 0.1%
4 12
 
< 0.1%
6 1
 
< 0.1%
884 1
 
< 0.1%
281 1
 
< 0.1%
12 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 152106
98.6%
1 2141
 
1.4%
2 13
 
< 0.1%
3 1
 
< 0.1%
4 12
 
< 0.1%
6 1
 
< 0.1%
12 1
 
< 0.1%
281 1
 
< 0.1%
884 1
 
< 0.1%
ValueCountFrequency (%)
884 1
 
< 0.1%
281 1
 
< 0.1%
12 1
 
< 0.1%
6 1
 
< 0.1%
4 12
 
< 0.1%
3 1
 
< 0.1%
2 13
 
< 0.1%
1 2141
 
1.4%
0 152106
98.6%

root_shell
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
154254 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154277
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154254
> 99.9%
1 23
 
< 0.1%

Length

2024-09-29T12:27:53.392462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:53.514710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 154254
> 99.9%
1 23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 154254
> 99.9%
1 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154254
> 99.9%
1 23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154254
> 99.9%
1 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154254
> 99.9%
1 23
 
< 0.1%

su_attempted
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
154274 
1
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154277
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154274
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%

Length

2024-09-29T12:27:53.638943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:53.762368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 154274
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 154274
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154274
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154274
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154274
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%

num_root
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013832263
Minimum0
Maximum993
Zeros154100
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:53.878487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum993
Range993
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6331489
Coefficient of variation (CV)190.36284
Kurtosis131898.34
Mean0.013832263
Median Absolute Deviation (MAD)0
Skewness355.27004
Sum2134
Variance6.933473
MonotonicityNot monotonic
2024-09-29T12:27:54.009016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 154100
99.9%
1 66
 
< 0.1%
9 60
 
< 0.1%
6 35
 
< 0.1%
2 6
 
< 0.1%
4 5
 
< 0.1%
5 3
 
< 0.1%
993 1
 
< 0.1%
278 1
 
< 0.1%
ValueCountFrequency (%)
0 154100
99.9%
1 66
 
< 0.1%
2 6
 
< 0.1%
4 5
 
< 0.1%
5 3
 
< 0.1%
6 35
 
< 0.1%
9 60
 
< 0.1%
278 1
 
< 0.1%
993 1
 
< 0.1%
ValueCountFrequency (%)
993 1
 
< 0.1%
278 1
 
< 0.1%
9 60
 
< 0.1%
6 35
 
< 0.1%
5 3
 
< 0.1%
4 5
 
< 0.1%
2 6
 
< 0.1%
1 66
 
< 0.1%
0 154100
99.9%

num_file_creations
Real number (ℝ)

SKEWED  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001276924
Minimum0
Maximum28
Zeros154194
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:54.126040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum28
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.12190711
Coefficient of variation (CV)95.469353
Kurtosis32319.644
Mean0.001276924
Median Absolute Deviation (MAD)0
Skewness170.94969
Sum197
Variance0.014861342
MonotonicityNot monotonic
2024-09-29T12:27:54.250330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 154194
99.9%
1 67
 
< 0.1%
2 8
 
< 0.1%
16 2
 
< 0.1%
4 2
 
< 0.1%
20 1
 
< 0.1%
5 1
 
< 0.1%
28 1
 
< 0.1%
21 1
 
< 0.1%
ValueCountFrequency (%)
0 154194
99.9%
1 67
 
< 0.1%
2 8
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
16 2
 
< 0.1%
20 1
 
< 0.1%
21 1
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
28 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
16 2
 
< 0.1%
5 1
 
< 0.1%
4 2
 
< 0.1%
2 8
 
< 0.1%
1 67
 
< 0.1%
0 154194
99.9%

num_shells
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
154266 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154277
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154266
> 99.9%
1 11
 
< 0.1%

Length

2024-09-29T12:27:54.389594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:54.514969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 154266
> 99.9%
1 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 154266
> 99.9%
1 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154266
> 99.9%
1 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154266
> 99.9%
1 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154266
> 99.9%
1 11
 
< 0.1%

num_access_files
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
154143 
1
 
127
2
 
5
8
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154277
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154143
99.9%
1 127
 
0.1%
2 5
 
< 0.1%
8 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-09-29T12:27:54.641342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:54.773093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 154143
99.9%
1 127
 
0.1%
2 5
 
< 0.1%
8 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 154143
99.9%
1 127
 
0.1%
2 5
 
< 0.1%
8 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154143
99.9%
1 127
 
0.1%
2 5
 
< 0.1%
8 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154143
99.9%
1 127
 
0.1%
2 5
 
< 0.1%
8 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154143
99.9%
1 127
 
0.1%
2 5
 
< 0.1%
8 1
 
< 0.1%
4 1
 
< 0.1%

num_outbound_cmds
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
154277 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154277
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154277
100.0%

Length

2024-09-29T12:27:54.924306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:55.041280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 154277
100.0%

Most occurring characters

ValueCountFrequency (%)
0 154277
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154277
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154277
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154277
100.0%

is_host_login
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
154277 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154277
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154277
100.0%

Length

2024-09-29T12:27:55.165533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:55.279962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 154277
100.0%

Most occurring characters

ValueCountFrequency (%)
0 154277
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154277
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154277
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154277
100.0%

is_guest_login
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
153852 
1
 
425

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters154277
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 153852
99.7%
1 425
 
0.3%

Length

2024-09-29T12:27:55.401787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T12:27:55.522851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 153852
99.7%
1 425
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 153852
99.7%
1 425
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 153852
99.7%
1 425
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 153852
99.7%
1 425
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 153852
99.7%
1 425
 
0.3%

count
Real number (ℝ)

HIGH CORRELATION 

Distinct450
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.33551
Minimum0
Maximum511
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:55.660041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1103
median509
Q3511
95-th percentile511
Maximum511
Range511
Interquartile range (IQR)408

Descriptive statistics

Standard deviation217.27714
Coefficient of variation (CV)0.67407139
Kurtosis-1.5702763
Mean322.33551
Median Absolute Deviation (MAD)2
Skewness-0.46191388
Sum49728955
Variance47209.356
MonotonicityNot monotonic
2024-09-29T12:27:55.828652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511 68785
44.6%
1 14065
 
9.1%
510 8137
 
5.3%
2 4292
 
2.8%
4 2057
 
1.3%
3 2026
 
1.3%
509 1846
 
1.2%
5 1601
 
1.0%
6 1124
 
0.7%
7 996
 
0.6%
Other values (440) 49348
32.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14065
9.1%
2 4292
 
2.8%
3 2026
 
1.3%
4 2057
 
1.3%
5 1601
 
1.0%
6 1124
 
0.7%
7 996
 
0.6%
8 874
 
0.6%
9 797
 
0.5%
ValueCountFrequency (%)
511 68785
44.6%
510 8137
 
5.3%
509 1846
 
1.2%
508 448
 
0.3%
507 160
 
0.1%
506 48
 
< 0.1%
505 26
 
< 0.1%
504 43
 
< 0.1%
503 10
 
< 0.1%
502 13
 
< 0.1%

srv_count
Real number (ℝ)

HIGH CORRELATION 

Distinct374
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.53773
Minimum0
Maximum511
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:55.998789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median509
Q3511
95-th percentile511
Maximum511
Range511
Interquartile range (IQR)502

Descriptive statistics

Standard deviation247.62327
Coefficient of variation (CV)0.87953849
Kurtosis-1.9569424
Mean281.53773
Median Absolute Deviation (MAD)2
Skewness-0.18147395
Sum43434797
Variance61317.285
MonotonicityNot monotonic
2024-09-29T12:27:56.167562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511 67820
44.0%
1 13843
 
9.0%
510 8095
 
5.2%
2 6389
 
4.1%
3 3832
 
2.5%
4 3390
 
2.2%
5 3031
 
2.0%
6 2669
 
1.7%
8 2488
 
1.6%
7 2449
 
1.6%
Other values (364) 40271
26.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 13843
9.0%
2 6389
4.1%
3 3832
 
2.5%
4 3390
 
2.2%
5 3031
 
2.0%
6 2669
 
1.7%
7 2449
 
1.6%
8 2488
 
1.6%
9 2262
 
1.5%
ValueCountFrequency (%)
511 67820
44.0%
510 8095
 
5.2%
509 1649
 
1.1%
508 396
 
0.3%
507 160
 
0.1%
506 48
 
< 0.1%
505 26
 
< 0.1%
504 43
 
< 0.1%
503 10
 
< 0.1%
502 13
 
< 0.1%

serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17080537
Minimum0
Maximum1
Zeros126408
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:56.336281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37464291
Coefficient of variation (CV)2.1933907
Kurtosis1.0908948
Mean0.17080537
Median Absolute Deviation (MAD)0
Skewness1.7554292
Sum26351.34
Variance0.14035731
MonotonicityNot monotonic
2024-09-29T12:27:56.516475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 126408
81.9%
1 25923
 
16.8%
0.08 132
 
0.1%
0.99 112
 
0.1%
0.01 109
 
0.1%
0.05 108
 
0.1%
0.14 105
 
0.1%
0.07 93
 
0.1%
0.13 88
 
0.1%
0.06 82
 
0.1%
Other values (82) 1117
 
0.7%
ValueCountFrequency (%)
0 126408
81.9%
0.01 109
 
0.1%
0.02 53
 
< 0.1%
0.03 56
 
< 0.1%
0.04 81
 
0.1%
0.05 108
 
0.1%
0.06 82
 
0.1%
0.07 93
 
0.1%
0.08 132
 
0.1%
0.09 80
 
0.1%
ValueCountFrequency (%)
1 25923
16.8%
0.99 112
 
0.1%
0.98 6
 
< 0.1%
0.97 5
 
< 0.1%
0.96 4
 
< 0.1%
0.95 3
 
< 0.1%
0.94 1
 
< 0.1%
0.93 3
 
< 0.1%
0.92 1
 
< 0.1%
0.91 1
 
< 0.1%

srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17042145
Minimum0
Maximum1
Zeros127642
Zeros (%)82.7%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:56.696674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37563993
Coefficient of variation (CV)2.2041823
Kurtosis1.0795166
Mean0.17042145
Median Absolute Deviation (MAD)0
Skewness1.7542417
Sum26292.11
Variance0.14110536
MonotonicityNot monotonic
2024-09-29T12:27:56.861439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 127642
82.7%
1 26220
 
17.0%
0.03 40
 
< 0.1%
0.05 38
 
< 0.1%
0.5 38
 
< 0.1%
0.02 34
 
< 0.1%
0.06 34
 
< 0.1%
0.08 24
 
< 0.1%
0.25 23
 
< 0.1%
0.17 21
 
< 0.1%
Other values (25) 163
 
0.1%
ValueCountFrequency (%)
0 127642
82.7%
0.01 5
 
< 0.1%
0.02 34
 
< 0.1%
0.03 40
 
< 0.1%
0.04 18
 
< 0.1%
0.05 38
 
< 0.1%
0.06 34
 
< 0.1%
0.07 17
 
< 0.1%
0.08 24
 
< 0.1%
0.09 10
 
< 0.1%
ValueCountFrequency (%)
1 26220
17.0%
0.95 1
 
< 0.1%
0.94 3
 
< 0.1%
0.93 1
 
< 0.1%
0.92 3
 
< 0.1%
0.91 1
 
< 0.1%
0.9 1
 
< 0.1%
0.86 1
 
< 0.1%
0.85 2
 
< 0.1%
0.75 1
 
< 0.1%

rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0665256
Minimum0
Maximum1
Zeros143479
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:57.030952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24638232
Coefficient of variation (CV)3.7035716
Kurtosis10.044635
Mean0.0665256
Median Absolute Deviation (MAD)0
Skewness3.4627943
Sum10263.37
Variance0.060704249
MonotonicityNot monotonic
2024-09-29T12:27:57.212497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 143479
93.0%
1 8931
 
5.8%
0.86 112
 
0.1%
0.87 102
 
0.1%
0.92 95
 
0.1%
0.9 91
 
0.1%
0.95 91
 
0.1%
0.25 85
 
0.1%
0.91 74
 
< 0.1%
0.88 68
 
< 0.1%
Other values (67) 1149
 
0.7%
ValueCountFrequency (%)
0 143479
93.0%
0.01 41
 
< 0.1%
0.02 30
 
< 0.1%
0.03 16
 
< 0.1%
0.04 8
 
< 0.1%
0.05 7
 
< 0.1%
0.06 7
 
< 0.1%
0.07 6
 
< 0.1%
0.08 5
 
< 0.1%
0.09 5
 
< 0.1%
ValueCountFrequency (%)
1 8931
5.8%
0.99 34
 
< 0.1%
0.98 34
 
< 0.1%
0.97 36
 
< 0.1%
0.96 65
 
< 0.1%
0.95 91
 
0.1%
0.94 63
 
< 0.1%
0.93 64
 
< 0.1%
0.92 95
 
0.1%
0.91 74
 
< 0.1%

srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.067316774
Minimum0
Maximum1
Zeros143204
Zeros (%)92.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:57.380534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24837337
Coefficient of variation (CV)3.6896208
Kurtosis10.065966
Mean0.067316774
Median Absolute Deviation (MAD)0
Skewness3.46542
Sum10385.43
Variance0.061689332
MonotonicityNot monotonic
2024-09-29T12:27:57.546718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 143204
92.8%
1 10115
 
6.6%
0.33 201
 
0.1%
0.5 140
 
0.1%
0.25 127
 
0.1%
0.2 116
 
0.1%
0.17 101
 
0.1%
0.14 34
 
< 0.1%
0.4 26
 
< 0.1%
0.29 23
 
< 0.1%
Other values (28) 190
 
0.1%
ValueCountFrequency (%)
0 143204
92.8%
0.01 1
 
< 0.1%
0.02 21
 
< 0.1%
0.03 13
 
< 0.1%
0.04 20
 
< 0.1%
0.05 14
 
< 0.1%
0.06 14
 
< 0.1%
0.07 15
 
< 0.1%
0.08 11
 
< 0.1%
0.09 7
 
< 0.1%
ValueCountFrequency (%)
1 10115
6.6%
0.88 1
 
< 0.1%
0.86 2
 
< 0.1%
0.83 1
 
< 0.1%
0.82 1
 
< 0.1%
0.81 1
 
< 0.1%
0.75 7
 
< 0.1%
0.71 1
 
< 0.1%
0.69 1
 
< 0.1%
0.67 18
 
< 0.1%

same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79218263
Minimum0
Maximum1
Zeros2050
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:57.714235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38793205
Coefficient of variation (CV)0.48970027
Kurtosis-0.14672838
Mean0.79218263
Median Absolute Deviation (MAD)0
Skewness-1.3500691
Sum122215.56
Variance0.15049127
MonotonicityNot monotonic
2024-09-29T12:27:57.889460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 119332
77.3%
0.06 3315
 
2.1%
0.05 3173
 
2.1%
0.04 3048
 
2.0%
0.07 3028
 
2.0%
0.03 2868
 
1.9%
0.02 2825
 
1.8%
0.01 2690
 
1.7%
0.08 2169
 
1.4%
0 2050
 
1.3%
Other values (85) 9779
 
6.3%
ValueCountFrequency (%)
0 2050
1.3%
0.01 2690
1.7%
0.02 2825
1.8%
0.03 2868
1.9%
0.04 3048
2.0%
0.05 3173
2.1%
0.06 3315
2.1%
0.07 3028
2.0%
0.08 2169
1.4%
0.09 1484
1.0%
ValueCountFrequency (%)
1 119332
77.3%
0.99 73
 
< 0.1%
0.98 65
 
< 0.1%
0.97 21
 
< 0.1%
0.96 7
 
< 0.1%
0.95 6
 
< 0.1%
0.94 2
 
< 0.1%
0.93 3
 
< 0.1%
0.92 4
 
< 0.1%
0.91 2
 
< 0.1%

diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.027600614
Minimum0
Maximum1
Zeros119317
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:58.063010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.07
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.11576546
Coefficient of variation (CV)4.1943072
Kurtosis58.208359
Mean0.027600614
Median Absolute Deviation (MAD)0
Skewness7.4846271
Sum4258.14
Variance0.013401641
MonotonicityNot monotonic
2024-09-29T12:27:58.434193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 119317
77.3%
0.06 15844
 
10.3%
0.07 8655
 
5.6%
0.05 5807
 
3.8%
1 1771
 
1.1%
0.08 954
 
0.6%
0.04 315
 
0.2%
0.5 192
 
0.1%
0.67 191
 
0.1%
0.09 140
 
0.1%
Other values (61) 1091
 
0.7%
ValueCountFrequency (%)
0 119317
77.3%
0.01 31
 
< 0.1%
0.02 34
 
< 0.1%
0.03 68
 
< 0.1%
0.04 315
 
0.2%
0.05 5807
 
3.8%
0.06 15844
 
10.3%
0.07 8655
 
5.6%
0.08 954
 
0.6%
0.09 140
 
0.1%
ValueCountFrequency (%)
1 1771
1.1%
0.99 8
 
< 0.1%
0.97 2
 
< 0.1%
0.96 32
 
< 0.1%
0.95 17
 
< 0.1%
0.89 2
 
< 0.1%
0.88 1
 
< 0.1%
0.83 1
 
< 0.1%
0.82 1
 
< 0.1%
0.8 2
 
< 0.1%

srv_diff_host_rate
Real number (ℝ)

ZEROS 

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.033352736
Minimum0
Maximum1
Zeros142812
Zeros (%)92.6%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:58.605844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.16
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1553149
Coefficient of variation (CV)4.6567365
Kurtosis29.126917
Mean0.033352736
Median Absolute Deviation (MAD)0
Skewness5.3955589
Sum5145.56
Variance0.024122718
MonotonicityNot monotonic
2024-09-29T12:27:58.782079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 142812
92.6%
1 3087
 
2.0%
0.67 560
 
0.4%
0.5 520
 
0.3%
0.12 466
 
0.3%
0.33 423
 
0.3%
0.11 372
 
0.2%
0.25 353
 
0.2%
0.1 338
 
0.2%
0.4 336
 
0.2%
Other values (48) 5010
 
3.2%
ValueCountFrequency (%)
0 142812
92.6%
0.01 122
 
0.1%
0.02 135
 
0.1%
0.03 55
 
< 0.1%
0.04 91
 
0.1%
0.05 162
 
0.1%
0.06 220
 
0.1%
0.07 271
 
0.2%
0.08 297
 
0.2%
0.09 320
 
0.2%
ValueCountFrequency (%)
1 3087
2.0%
0.86 2
 
< 0.1%
0.83 5
 
< 0.1%
0.8 25
 
< 0.1%
0.75 110
 
0.1%
0.71 4
 
< 0.1%
0.7 1
 
< 0.1%
0.67 560
 
0.4%
0.64 2
 
< 0.1%
0.62 6
 
< 0.1%

dst_host_count
Real number (ℝ)

HIGH CORRELATION 

Distinct256
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.06577
Minimum0
Maximum255
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:58.954964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q1255
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)0

Descriptive statistics

Standard deviation68.285716
Coefficient of variation (CV)0.29680954
Kurtosis4.9406139
Mean230.06577
Median Absolute Deviation (MAD)0
Skewness-2.5668131
Sum35493857
Variance4662.939
MonotonicityNot monotonic
2024-09-29T12:27:59.129638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 133454
86.5%
1 1643
 
1.1%
2 908
 
0.6%
3 537
 
0.3%
4 512
 
0.3%
5 379
 
0.2%
6 330
 
0.2%
8 284
 
0.2%
7 278
 
0.2%
9 270
 
0.2%
Other values (246) 15682
 
10.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 1643
1.1%
2 908
0.6%
3 537
 
0.3%
4 512
 
0.3%
5 379
 
0.2%
6 330
 
0.2%
7 278
 
0.2%
8 284
 
0.2%
9 270
 
0.2%
ValueCountFrequency (%)
255 133454
86.5%
254 28
 
< 0.1%
253 31
 
< 0.1%
252 29
 
< 0.1%
251 27
 
< 0.1%
250 27
 
< 0.1%
249 17
 
< 0.1%
248 27
 
< 0.1%
247 27
 
< 0.1%
246 16
 
< 0.1%

dst_host_srv_count
Real number (ℝ)

HIGH CORRELATION 

Distinct256
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.94691
Minimum0
Maximum255
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:59.296613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q125
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)230

Descriptive statistics

Standard deviation107.56892
Coefficient of variation (CV)0.58162051
Kurtosis-1.0395803
Mean184.94691
Median Absolute Deviation (MAD)0
Skewness-0.94883184
Sum28533054
Variance11571.071
MonotonicityNot monotonic
2024-09-29T12:27:59.467927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 102814
66.6%
1 5312
 
3.4%
2 2351
 
1.5%
3 1817
 
1.2%
10 1721
 
1.1%
11 1703
 
1.1%
5 1699
 
1.1%
8 1695
 
1.1%
9 1666
 
1.1%
4 1620
 
1.1%
Other values (246) 31879
 
20.7%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 5312
3.4%
2 2351
1.5%
3 1817
 
1.2%
4 1620
 
1.1%
5 1699
 
1.1%
6 1616
 
1.0%
7 1599
 
1.0%
8 1695
 
1.1%
9 1666
 
1.1%
ValueCountFrequency (%)
255 102814
66.6%
254 363
 
0.2%
253 233
 
0.2%
252 168
 
0.1%
251 194
 
0.1%
250 219
 
0.1%
249 168
 
0.1%
248 116
 
0.1%
247 120
 
0.1%
246 132
 
0.1%

dst_host_same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74622601
Minimum0
Maximum1
Zeros5104
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:59.637725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.25
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.41469558
Coefficient of variation (CV)0.55572383
Kurtosis-0.7855258
Mean0.74622601
Median Absolute Deviation (MAD)0
Skewness-1.0796608
Sum115125.51
Variance0.17197242
MonotonicityNot monotonic
2024-09-29T12:27:59.810469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 107402
69.6%
0 5104
 
3.3%
0.04 4920
 
3.2%
0.02 4777
 
3.1%
0.05 4632
 
3.0%
0.07 4621
 
3.0%
0.01 4034
 
2.6%
0.03 3211
 
2.1%
0.06 3046
 
2.0%
0.08 1787
 
1.2%
Other values (91) 10743
 
7.0%
ValueCountFrequency (%)
0 5104
3.3%
0.01 4034
2.6%
0.02 4777
3.1%
0.03 3211
2.1%
0.04 4920
3.2%
0.05 4632
3.0%
0.06 3046
2.0%
0.07 4621
3.0%
0.08 1787
 
1.2%
0.09 900
 
0.6%
ValueCountFrequency (%)
1 107402
69.6%
0.99 344
 
0.2%
0.98 515
 
0.3%
0.97 240
 
0.2%
0.96 445
 
0.3%
0.95 399
 
0.3%
0.94 333
 
0.2%
0.93 330
 
0.2%
0.92 277
 
0.2%
0.91 283
 
0.2%

dst_host_diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.041634528
Minimum0
Maximum1
Zeros107167
Zeros (%)69.5%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:27:59.980251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.05
95-th percentile0.08
Maximum1
Range1
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.14671162
Coefficient of variation (CV)3.523797
Kurtosis30.12018
Mean0.041634528
Median Absolute Deviation (MAD)0
Skewness5.468846
Sum6423.25
Variance0.0215243
MonotonicityNot monotonic
2024-09-29T12:28:00.155509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 107167
69.5%
0.07 13620
 
8.8%
0.06 8575
 
5.6%
0.05 5569
 
3.6%
0.08 4461
 
2.9%
0.01 4259
 
2.8%
1 1985
 
1.3%
0.02 1755
 
1.1%
0.09 1168
 
0.8%
0.03 1113
 
0.7%
Other values (91) 4605
 
3.0%
ValueCountFrequency (%)
0 107167
69.5%
0.01 4259
 
2.8%
0.02 1755
 
1.1%
0.03 1113
 
0.7%
0.04 927
 
0.6%
0.05 5569
 
3.6%
0.06 8575
 
5.6%
0.07 13620
 
8.8%
0.08 4461
 
2.9%
0.09 1168
 
0.8%
ValueCountFrequency (%)
1 1985
1.3%
0.99 6
 
< 0.1%
0.98 12
 
< 0.1%
0.97 10
 
< 0.1%
0.96 10
 
< 0.1%
0.95 14
 
< 0.1%
0.94 9
 
< 0.1%
0.93 7
 
< 0.1%
0.92 6
 
< 0.1%
0.91 41
 
< 0.1%

dst_host_same_src_port_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59397182
Minimum0
Maximum1
Zeros45251
Zeros (%)29.3%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:28:00.326200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.48195008
Coefficient of variation (CV)0.81140227
Kurtosis-1.8430286
Mean0.59397182
Median Absolute Deviation (MAD)0
Skewness-0.36574494
Sum91636.19
Variance0.23227588
MonotonicityNot monotonic
2024-09-29T12:28:00.499229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 88756
57.5%
0 45251
29.3%
0.01 6861
 
4.4%
0.02 2268
 
1.5%
0.03 1371
 
0.9%
0.04 882
 
0.6%
0.05 730
 
0.5%
0.06 550
 
0.4%
0.5 519
 
0.3%
0.07 478
 
0.3%
Other values (91) 6611
 
4.3%
ValueCountFrequency (%)
0 45251
29.3%
0.01 6861
 
4.4%
0.02 2268
 
1.5%
0.03 1371
 
0.9%
0.04 882
 
0.6%
0.05 730
 
0.5%
0.06 550
 
0.4%
0.07 478
 
0.3%
0.08 469
 
0.3%
0.09 277
 
0.2%
ValueCountFrequency (%)
1 88756
57.5%
0.99 187
 
0.1%
0.98 134
 
0.1%
0.97 65
 
< 0.1%
0.96 81
 
0.1%
0.95 71
 
< 0.1%
0.94 30
 
< 0.1%
0.93 40
 
< 0.1%
0.92 36
 
< 0.1%
0.91 37
 
< 0.1%

dst_host_srv_diff_host_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010675927
Minimum0
Maximum1
Zeros137157
Zeros (%)88.9%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:28:00.668543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.04
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.065911141
Coefficient of variation (CV)6.1738096
Kurtosis130.1286
Mean0.010675927
Median Absolute Deviation (MAD)0
Skewness10.494109
Sum1647.05
Variance0.0043442785
MonotonicityNot monotonic
2024-09-29T12:28:00.845597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 137157
88.9%
0.02 3529
 
2.3%
0.01 3152
 
2.0%
0.04 2074
 
1.3%
0.03 1995
 
1.3%
0.05 1377
 
0.9%
0.5 724
 
0.5%
0.06 612
 
0.4%
0.07 483
 
0.3%
1 337
 
0.2%
Other values (49) 2837
 
1.8%
ValueCountFrequency (%)
0 137157
88.9%
0.01 3152
 
2.0%
0.02 3529
 
2.3%
0.03 1995
 
1.3%
0.04 2074
 
1.3%
0.05 1377
 
0.9%
0.06 612
 
0.4%
0.07 483
 
0.3%
0.08 221
 
0.1%
0.09 209
 
0.1%
ValueCountFrequency (%)
1 337
0.2%
0.67 20
 
< 0.1%
0.62 1
 
< 0.1%
0.6 10
 
< 0.1%
0.58 1
 
< 0.1%
0.57 9
 
< 0.1%
0.56 8
 
< 0.1%
0.55 6
 
< 0.1%
0.54 8
 
< 0.1%
0.53 19
 
< 0.1%

dst_host_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17068319
Minimum0
Maximum1
Zeros124366
Zeros (%)80.6%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:28:01.019773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37421913
Coefficient of variation (CV)2.192478
Kurtosis1.1052058
Mean0.17068319
Median Absolute Deviation (MAD)0
Skewness1.7594208
Sum26332.49
Variance0.14003995
MonotonicityNot monotonic
2024-09-29T12:28:01.196487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 124366
80.6%
1 25982
 
16.8%
0.01 1449
 
0.9%
0.02 454
 
0.3%
0.09 262
 
0.2%
0.03 202
 
0.1%
0.05 135
 
0.1%
0.08 110
 
0.1%
0.04 105
 
0.1%
0.07 102
 
0.1%
Other values (80) 1110
 
0.7%
ValueCountFrequency (%)
0 124366
80.6%
0.01 1449
 
0.9%
0.02 454
 
0.3%
0.03 202
 
0.1%
0.04 105
 
0.1%
0.05 135
 
0.1%
0.06 67
 
< 0.1%
0.07 102
 
0.1%
0.08 110
 
0.1%
0.09 262
 
0.2%
ValueCountFrequency (%)
1 25982
16.8%
0.99 11
 
< 0.1%
0.98 4
 
< 0.1%
0.97 4
 
< 0.1%
0.96 9
 
< 0.1%
0.95 4
 
< 0.1%
0.94 7
 
< 0.1%
0.93 3
 
< 0.1%
0.92 3
 
< 0.1%
0.91 2
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17020236
Minimum0
Maximum1
Zeros125858
Zeros (%)81.6%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:28:01.367317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37545583
Coefficient of variation (CV)2.2059378
Kurtosis1.0876668
Mean0.17020236
Median Absolute Deviation (MAD)0
Skewness1.7568987
Sum26258.31
Variance0.14096708
MonotonicityNot monotonic
2024-09-29T12:28:01.541008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 125858
81.6%
1 26194
 
17.0%
0.01 1720
 
1.1%
0.02 266
 
0.2%
0.03 69
 
< 0.1%
0.04 37
 
< 0.1%
0.05 26
 
< 0.1%
0.06 15
 
< 0.1%
0.5 9
 
< 0.1%
0.07 9
 
< 0.1%
Other values (38) 74
 
< 0.1%
ValueCountFrequency (%)
0 125858
81.6%
0.01 1720
 
1.1%
0.02 266
 
0.2%
0.03 69
 
< 0.1%
0.04 37
 
< 0.1%
0.05 26
 
< 0.1%
0.06 15
 
< 0.1%
0.07 9
 
< 0.1%
0.08 8
 
< 0.1%
0.09 3
 
< 0.1%
ValueCountFrequency (%)
1 26194
17.0%
0.97 2
 
< 0.1%
0.96 4
 
< 0.1%
0.91 2
 
< 0.1%
0.88 1
 
< 0.1%
0.85 1
 
< 0.1%
0.84 1
 
< 0.1%
0.83 2
 
< 0.1%
0.8 1
 
< 0.1%
0.78 1
 
< 0.1%

dst_host_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06791103
Minimum0
Maximum1
Zeros139900
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:28:01.711015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24486307
Coefficient of variation (CV)3.6056451
Kurtosis9.9084258
Mean0.06791103
Median Absolute Deviation (MAD)0
Skewness3.4317133
Sum10477.11
Variance0.059957923
MonotonicityNot monotonic
2024-09-29T12:28:01.886093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 139900
90.7%
1 8446
 
5.5%
0.01 700
 
0.5%
0.05 647
 
0.4%
0.04 646
 
0.4%
0.02 490
 
0.3%
0.03 329
 
0.2%
0.06 181
 
0.1%
0.85 115
 
0.1%
0.93 104
 
0.1%
Other values (91) 2719
 
1.8%
ValueCountFrequency (%)
0 139900
90.7%
0.01 700
 
0.5%
0.02 490
 
0.3%
0.03 329
 
0.2%
0.04 646
 
0.4%
0.05 647
 
0.4%
0.06 181
 
0.1%
0.07 49
 
< 0.1%
0.08 41
 
< 0.1%
0.09 26
 
< 0.1%
ValueCountFrequency (%)
1 8446
5.5%
0.99 60
 
< 0.1%
0.98 56
 
< 0.1%
0.97 58
 
< 0.1%
0.96 69
 
< 0.1%
0.95 100
 
0.1%
0.94 46
 
< 0.1%
0.93 104
 
0.1%
0.92 91
 
0.1%
0.91 71
 
< 0.1%

dst_host_srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.066511405
Minimum0
Maximum1
Zeros140793
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-09-29T12:28:02.061000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24603478
Coefficient of variation (CV)3.6991367
Kurtosis10.238846
Mean0.066511405
Median Absolute Deviation (MAD)0
Skewness3.4909747
Sum10261.18
Variance0.060533111
MonotonicityNot monotonic
2024-09-29T12:28:02.241907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 140793
91.3%
1 9373
 
6.1%
0.01 690
 
0.4%
0.04 652
 
0.4%
0.05 646
 
0.4%
0.02 443
 
0.3%
0.03 337
 
0.2%
0.06 177
 
0.1%
0.98 102
 
0.1%
0.99 73
 
< 0.1%
Other values (91) 991
 
0.6%
ValueCountFrequency (%)
0 140793
91.3%
0.01 690
 
0.4%
0.02 443
 
0.3%
0.03 337
 
0.2%
0.04 652
 
0.4%
0.05 646
 
0.4%
0.06 177
 
0.1%
0.07 35
 
< 0.1%
0.08 23
 
< 0.1%
0.09 9
 
< 0.1%
ValueCountFrequency (%)
1 9373
6.1%
0.99 73
 
< 0.1%
0.98 102
 
0.1%
0.97 36
 
< 0.1%
0.96 36
 
< 0.1%
0.95 35
 
< 0.1%
0.94 36
 
< 0.1%
0.93 31
 
< 0.1%
0.92 20
 
< 0.1%
0.91 22
 
< 0.1%

label
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
smurf.
84162 
neptune.
32109 
normal.
29309 
back.
 
2203
satan.
 
1589
Other values (10)
 
4905

Length

Max length16
Median length6
Mean length6.6938559
Min length4

Characters and Unicode

Total characters1032708
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsmurf.
2nd rowsmurf.
3rd rowneptune.
4th rowneptune.
5th rowsmurf.

Common Values

ValueCountFrequency (%)
smurf. 84162
54.6%
neptune. 32109
 
20.8%
normal. 29309
 
19.0%
back. 2203
 
1.4%
satan. 1589
 
1.0%
ipsweep. 1247
 
0.8%
portsweep. 1040
 
0.7%
warezclient. 1020
 
0.7%
teardrop. 979
 
0.6%
pod. 264
 
0.2%
Other values (5) 355
 
0.2%

Length

2024-09-29T12:28:02.410342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
smurf 84162
54.6%
neptune 32109
 
20.8%
normal 29309
 
19.0%
back 2203
 
1.4%
satan 1589
 
1.0%
ipsweep 1247
 
0.8%
portsweep 1040
 
0.7%
warezclient 1020
 
0.7%
teardrop 979
 
0.6%
pod 264
 
0.2%
Other values (5) 355
 
0.2%

Most occurring characters

ValueCountFrequency (%)
. 154277
14.9%
r 117589
11.4%
u 116354
11.3%
m 113722
11.0%
n 96388
9.3%
s 88270
8.5%
f 84252
8.2%
e 71964
7.0%
p 38210
 
3.7%
a 37034
 
3.6%
Other values (13) 114648
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1032708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 154277
14.9%
r 117589
11.4%
u 116354
11.3%
m 113722
11.0%
n 96388
9.3%
s 88270
8.5%
f 84252
8.2%
e 71964
7.0%
p 38210
 
3.7%
a 37034
 
3.6%
Other values (13) 114648
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1032708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 154277
14.9%
r 117589
11.4%
u 116354
11.3%
m 113722
11.0%
n 96388
9.3%
s 88270
8.5%
f 84252
8.2%
e 71964
7.0%
p 38210
 
3.7%
a 37034
 
3.6%
Other values (13) 114648
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1032708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 154277
14.9%
r 117589
11.4%
u 116354
11.3%
m 113722
11.0%
n 96388
9.3%
s 88270
8.5%
f 84252
8.2%
e 71964
7.0%
p 38210
 
3.7%
a 37034
 
3.6%
Other values (13) 114648
11.1%

Interactions

2024-09-29T12:27:43.829115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:19.428132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:22.825101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:26.170661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:29.365216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:32.788795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:35.928193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:39.344485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:42.585442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:45.872773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:49.368867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:52.748767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:56.057009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:59.455964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:02.730855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:06.180438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:09.506669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:12.886370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:16.538719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:19.901632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:23.183463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:26.749282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:30.025983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:33.542645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:37.078478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:40.376704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:43.946804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:19.597651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:22.953277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:26.288113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:29.668670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:32.902984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:36.047245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:39.466307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:42.706886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:45.991142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:49.487950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:52.867705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:56.174766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:59.575122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:02.851525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:06.301390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:09.643330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:13.007871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:16.669276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:20.021263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:23.308541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:26.871357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:30.157949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:33.665393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:37.197713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:40.497220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:44.078331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:19.728770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:23.088965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:26.418686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:29.801516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:33.032509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:36.179036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:39.598142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:42.840013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:46.125990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:49.627639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:53.011430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:56.304387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:59.717891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:02.986896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:06.435285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:09.782635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:13.148754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:16.812549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:20.157853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:23.442588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:27.006370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:30.298364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:33.802786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:37.335434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:40.630499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:44.201402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:19.859191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:23.216736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:26.538763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:29.924492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:33.156571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:36.304054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:39.722410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:42.967675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:46.250649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:49.757505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:53.141812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:56.427573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:59.843250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:03.114821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:06.559731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:09.917376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:13.276071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:16.948646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:20.285683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:23.570933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:27.132828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:30.425319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:33.929651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:37.460727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:40.770592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:44.329197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:19.983326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:23.347016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:26.663505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:30.049233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:33.285513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:36.428662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:39.847302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:43.095009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:46.380780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:49.886586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:53.269485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:56.550796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:59.969667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:03.240936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:06.692274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:10.048675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:13.415440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:17.084120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:20.415138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:23.699417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:27.263876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:30.555447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:34.328737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:37.588644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:40.897343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:44.447167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:20.100106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:23.470912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:26.779718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:30.167704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:33.398957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:36.546737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:39.985415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:43.216592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:46.500553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:50.006092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:53.390007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:56.876706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:00.105564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:03.361711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:06.811376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:10.172873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:13.536706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:17.208020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:20.535650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:23.821057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:27.386286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:30.680296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:34.453580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:37.708426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:41.018340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:44.570227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:20.219974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:23.597198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:26.901123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:30.291621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:33.517889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:36.665399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:40.108487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:43.341406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:46.623804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:50.135025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:53.514457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:56.996915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:00.228897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:03.485374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:06.935499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-29T12:27:46.559098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:22.331225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:25.660227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:28.856441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:32.279357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:35.442681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:38.850998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:42.078747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:45.369040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:48.865573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:52.230629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:55.561973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:58.960842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:02.217129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:05.480108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:08.983058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:12.365896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:15.997111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:19.397342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:22.674810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:26.244292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:29.526432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:33.016758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:36.580382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:39.875819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:43.140871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:46.682392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:22.453718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:25.786239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:28.978431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:32.406461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:35.564477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:38.975237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:42.202436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:45.492837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:48.990634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:52.364525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:55.687063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:59.088340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:02.352962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:05.604254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:09.112426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:12.497403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:16.137175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:19.525093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:22.806383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:26.370716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:29.649484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:33.144510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:36.703122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:40.001722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:43.449382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:46.804741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:22.582377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:25.914054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:29.115296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:32.531098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:35.684475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:39.098456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:42.331244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:45.620912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:49.119483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:52.499066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:55.809963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:59.211706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:02.478059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:05.729475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:09.242141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:12.627745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:16.276262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:19.650524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:22.931366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:26.495654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:29.775869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:33.284653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:36.829136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:40.126153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:43.585152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:46.927284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:22.704706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:26.040721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:29.238624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:32.663440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:35.806891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:39.221933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:42.456448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:45.744778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:49.242975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:52.623191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:55.935283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:26:59.334648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:02.608674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:05.853407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:09.369136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:12.755250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:16.411063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:19.777195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:23.057628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:26.622380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:29.900648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:33.412974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:36.953672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:40.254147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-29T12:27:43.705897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-29T12:28:02.800847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
countdiff_srv_ratedst_bytesdst_host_countdst_host_diff_srv_ratedst_host_rerror_ratedst_host_same_src_port_ratedst_host_same_srv_ratedst_host_serror_ratedst_host_srv_countdst_host_srv_diff_host_ratedst_host_srv_rerror_ratedst_host_srv_serror_ratedurationflaghotis_guest_loginlabellandlogged_innum_access_filesnum_compromisednum_failed_loginsnum_file_creationsnum_rootnum_shellsprotocol_typererror_rateroot_shellsame_srv_rateserror_ratesrc_bytessrv_countsrv_diff_host_ratesrv_rerror_ratesrv_serror_ratesu_attemptedwrong_fragment
count1.000-0.302-0.6250.558-0.504-0.2780.7260.497-0.2910.565-0.497-0.268-0.298-0.2600.314-0.1930.0950.4720.0190.7810.025-0.1640.022-0.035-0.0530.0130.707-0.2020.0210.287-0.2540.5970.931-0.384-0.215-0.2660.0000.215
diff_srv_rate-0.3021.000-0.2280.1960.8350.300-0.709-0.8530.794-0.819-0.1830.2930.764-0.0510.144-0.0690.0030.2880.0000.0400.000-0.0620.000-0.006-0.0120.0250.1590.3670.000-0.9830.823-0.731-0.487-0.1440.3440.7880.0000.005
dst_bytes-0.625-0.2281.000-0.557-0.0590.053-0.4060.077-0.1620.0410.5080.059-0.1500.2840.0740.3200.0000.3890.0000.0170.0000.2950.0000.030-0.0040.0000.010-0.1000.0000.235-0.209-0.087-0.4860.480-0.065-0.1980.0000.000
dst_host_count0.5580.196-0.5571.0000.062-0.1000.153-0.0880.1300.044-0.903-0.1010.118-0.1520.067-0.0940.0940.2720.0490.6470.017-0.0640.053-0.047-0.0740.0360.276-0.0420.044-0.2000.1720.1120.439-0.443-0.0490.1640.0200.091
dst_host_diff_srv_rate-0.5040.835-0.0590.0621.0000.318-0.736-0.9810.703-0.942-0.1030.2870.6600.2200.188-0.0500.0220.3150.0260.1120.029-0.0750.0000.0230.0440.0100.3410.3570.000-0.8290.704-0.711-0.658-0.0690.3380.6710.0000.132
dst_host_rerror_rate-0.2780.3000.053-0.1000.3181.000-0.320-0.305-0.047-0.3410.0630.942-0.1120.0080.3290.3230.0120.2860.0000.1250.0000.3290.047-0.002-0.0040.0000.2560.8730.041-0.281-0.076-0.257-0.3650.0130.869-0.1310.0000.256
dst_host_same_src_port_rate0.726-0.709-0.4060.153-0.736-0.3201.0000.742-0.6490.731-0.132-0.314-0.630-0.0620.229-0.1500.0650.3580.0030.4840.017-0.1390.015-0.013-0.0150.0530.692-0.2760.0410.717-0.6380.7520.784-0.197-0.283-0.6220.0000.265
dst_host_same_srv_rate0.497-0.8530.077-0.088-0.981-0.3050.7421.000-0.7250.9570.123-0.281-0.686-0.2030.3070.0490.2970.3690.0030.3390.0360.0750.003-0.018-0.0370.0450.530-0.3510.0060.858-0.7300.7270.6650.086-0.332-0.7000.0350.301
dst_host_serror_rate-0.2910.794-0.1620.1300.703-0.047-0.649-0.7251.000-0.699-0.127-0.0660.951-0.0550.351-0.0060.0540.3640.0390.1960.000-0.0110.028-0.000-0.0070.0000.399-0.0600.000-0.8180.964-0.629-0.430-0.102-0.0640.9470.0520.134
dst_host_srv_count0.565-0.8190.0410.044-0.942-0.3410.7310.957-0.6991.0000.035-0.320-0.664-0.2120.302-0.0130.1850.3740.0190.3600.0210.0150.059-0.027-0.0350.0470.537-0.3710.0140.823-0.7010.7300.7200.042-0.354-0.6720.0190.247
dst_host_srv_diff_host_rate-0.497-0.1830.508-0.903-0.1030.063-0.1320.123-0.1270.0351.0000.072-0.1070.0910.088-0.0340.0030.3950.1510.0700.000-0.0400.0380.0230.0580.0000.0960.0420.0470.185-0.157-0.138-0.3780.4410.042-0.1480.0000.157
dst_host_srv_rerror_rate-0.2680.2930.059-0.1010.2870.942-0.314-0.281-0.066-0.3200.0721.000-0.114-0.0050.3430.3130.0140.1910.0000.1280.0000.3430.060-0.003-0.0090.0000.2350.8740.069-0.277-0.081-0.256-0.3550.0190.894-0.1380.0000.015
dst_host_srv_serror_rate-0.2980.764-0.1500.1180.660-0.112-0.630-0.6860.951-0.664-0.107-0.1141.000-0.0550.407-0.0130.0230.3310.4330.1960.000-0.0080.136-0.002-0.0060.0000.394-0.1110.000-0.7890.946-0.612-0.407-0.092-0.1200.9650.1380.028
duration-0.260-0.0510.284-0.1520.2200.008-0.062-0.203-0.055-0.2120.091-0.005-0.0551.0000.0950.1200.0760.0970.0000.0110.0690.0030.0000.0620.0130.0000.146-0.0110.0210.062-0.0760.027-0.2510.106-0.013-0.0740.1160.000
flag0.3140.1440.0740.0670.1880.3290.2290.3070.3510.3020.0880.3430.4070.0951.0000.0170.0280.4840.0250.2400.0020.0000.3050.0000.0000.0000.4850.3430.0000.3210.3690.0650.2060.0440.3400.4120.0000.035
hot-0.193-0.0690.320-0.094-0.0500.323-0.1500.049-0.006-0.013-0.0340.313-0.0130.1200.0171.0000.9710.1930.0000.1240.0000.8700.0000.026-0.0000.0000.0460.0200.0390.070-0.0600.219-0.1810.0540.086-0.0580.0000.000
is_guest_login0.0950.0030.0000.0940.0220.0120.0650.2970.0540.1850.0030.0140.0230.0760.0280.9711.0000.4660.0000.1210.0000.0000.0000.0340.0000.0000.0650.0130.0000.0320.0230.0000.0580.0100.0120.0230.0000.003
label0.4720.2880.3890.2720.3150.2860.3580.3690.3640.3740.3950.1910.3310.0970.4840.1930.4661.0001.0000.8440.0290.0000.7000.1310.0000.0150.7800.3020.6850.3410.3670.0290.3730.2320.2340.3100.0000.985
land0.0190.0000.0000.0490.0260.0000.0030.0030.0390.0190.1510.0000.4330.0000.0250.0000.0001.0001.0000.0030.0000.0000.0000.0000.0000.0000.0140.0320.0000.0100.0390.0000.0100.0650.0000.0250.0000.000
logged_in0.7810.0400.0170.6470.1120.1250.4840.3390.1960.3600.0700.1280.1960.0110.2400.1240.1210.8440.0031.0000.0680.0070.0070.0160.0070.0180.5340.1460.0270.2290.1960.0000.4770.4300.1920.2040.0100.039
num_access_files0.0250.0000.0000.0170.0290.0000.0170.0360.0000.0210.0000.0000.0000.0690.0020.0000.0000.0290.0000.0681.0000.7070.0000.5000.7070.0000.0250.0000.2080.0080.0040.0000.0140.0160.0230.0060.7070.000
num_compromised-0.164-0.0620.295-0.064-0.0750.329-0.1390.075-0.0110.015-0.0400.343-0.0080.0030.0000.8700.0000.0000.0000.0070.7071.0000.0000.0330.0160.0000.0000.0090.2080.063-0.0540.212-0.1520.0630.084-0.0530.8660.000
num_failed_logins0.0220.0000.0000.0530.0000.0470.0150.0030.0280.0590.0380.0600.1360.0000.3050.0000.0000.7000.0000.0070.0000.0001.0000.0000.0000.0000.0160.0540.0000.0000.0260.0000.0120.0000.0480.0320.0000.000
num_file_creations-0.035-0.0060.030-0.0470.023-0.002-0.013-0.018-0.000-0.0270.023-0.003-0.0020.0620.0000.0260.0340.1310.0000.0160.5000.0330.0001.0000.0410.0000.004-0.0050.0000.008-0.0080.030-0.0330.006-0.004-0.0100.0000.000
num_root-0.053-0.012-0.004-0.0740.044-0.004-0.015-0.037-0.007-0.0350.058-0.009-0.0060.0130.000-0.0000.0000.0000.0000.0070.7070.0160.0000.0411.0000.0000.000-0.0090.2080.014-0.016-0.000-0.0510.005-0.009-0.0150.8660.000
num_shells0.0130.0250.0000.0360.0100.0000.0530.0450.0000.0470.0000.0000.0000.0000.0000.0000.0000.0150.0000.0180.0000.0000.0000.0000.0001.0000.0100.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.000
protocol_type0.7070.1590.0100.2760.3410.2560.6920.5300.3990.5370.0960.2350.3940.1460.4850.0460.0650.7800.0140.5340.0250.0000.0160.0040.0000.0101.0000.2370.0150.4710.4050.0000.7210.1710.2410.3960.0010.261
rerror_rate-0.2020.367-0.100-0.0420.3570.873-0.276-0.351-0.060-0.3710.0420.874-0.111-0.0110.3430.0200.0130.3020.0320.1460.0000.0090.054-0.005-0.0090.0000.2371.0000.027-0.348-0.053-0.358-0.314-0.0300.962-0.1110.0000.016
root_shell0.0210.0000.0000.0440.0000.0410.0410.0060.0000.0140.0470.0690.0000.0210.0000.0390.0000.6850.0000.0270.2080.2080.0000.0000.2080.0000.0150.0271.0000.0060.0000.0000.0110.0000.0000.0000.2080.000
same_srv_rate0.287-0.9830.235-0.200-0.829-0.2810.7170.858-0.8180.8230.185-0.277-0.7890.0620.3210.0700.0320.3410.0100.2290.0080.0630.0000.0080.0140.0000.471-0.3480.0061.000-0.8490.7360.4910.146-0.326-0.8140.0000.052
serror_rate-0.2540.823-0.2090.1720.704-0.076-0.638-0.7300.964-0.701-0.157-0.0810.946-0.0760.369-0.0600.0230.3670.0390.1960.004-0.0540.026-0.008-0.0160.0000.405-0.0530.000-0.8491.000-0.649-0.404-0.124-0.0630.9770.0000.190
src_bytes0.597-0.731-0.0870.112-0.711-0.2570.7520.727-0.6290.730-0.138-0.256-0.6120.0270.0650.2190.0000.0290.0000.0000.0000.2120.0000.030-0.0000.0000.000-0.3580.0000.736-0.6491.0000.671-0.083-0.332-0.6320.0000.000
srv_count0.931-0.487-0.4860.439-0.658-0.3650.7840.665-0.4300.720-0.378-0.355-0.407-0.2510.206-0.1810.0580.3730.0100.4770.014-0.1520.012-0.033-0.0510.0050.721-0.3140.0110.491-0.4040.6711.000-0.223-0.317-0.3810.0000.361
srv_diff_host_rate-0.384-0.1440.480-0.443-0.0690.013-0.1970.086-0.1020.0420.4410.019-0.0920.1060.0440.0540.0100.2320.0650.4300.0160.0630.0000.0060.0050.0000.171-0.0300.0000.146-0.124-0.083-0.2231.0000.019-0.1130.0040.196
srv_rerror_rate-0.2150.344-0.065-0.0490.3380.869-0.283-0.332-0.064-0.3540.0420.894-0.120-0.0130.3400.0860.0120.2340.0000.1920.0230.0840.048-0.004-0.0090.0000.2410.9620.000-0.326-0.063-0.332-0.3170.0191.000-0.1240.0000.016
srv_serror_rate-0.2660.788-0.1980.1640.671-0.131-0.622-0.7000.947-0.672-0.148-0.1380.965-0.0740.412-0.0580.0230.3100.0250.2040.006-0.0530.032-0.010-0.0150.0000.396-0.1110.000-0.8140.977-0.632-0.381-0.113-0.1241.0000.0000.028
su_attempted0.0000.0000.0000.0200.0000.0000.0000.0350.0520.0190.0000.0000.1380.1160.0000.0000.0000.0000.0000.0100.7070.8660.0000.0000.8660.0000.0010.0000.2080.0000.0000.0000.0000.0040.0000.0001.0000.000
wrong_fragment0.2150.0050.0000.0910.1320.2560.2650.3010.1340.2470.1570.0150.0280.0000.0350.0000.0030.9850.0000.0390.0000.0000.0000.0000.0000.0000.2610.0160.0000.0520.1900.0000.3610.1960.0160.0280.0001.000

Missing values

2024-09-29T12:27:47.221576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-29T12:27:48.126072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratelabel
3390430icmpecr_iSF1032000000000000000005115110.00.00.00.01.000.000.002552551.000.001.000.000.00.00.00.0smurf.
2286940icmpecr_iSF1032000000000000000005105100.00.00.00.01.000.000.002552551.000.001.000.000.00.00.00.0smurf.
3553770tcpprivateS0000000000000000000136161.01.00.00.00.120.050.0025540.020.080.000.001.01.00.00.0neptune.
4714500tcpprivateREJ00000000000000000013740.00.01.01.00.030.060.0025540.020.050.000.000.00.01.01.0neptune.
2426960icmpecr_iSF1032000000000000000005115110.00.00.00.01.000.000.002552551.000.001.000.000.00.00.00.0smurf.
4023570icmpecr_iSF520000000000000000004634630.00.00.00.01.000.000.002552551.000.001.000.000.00.00.00.0smurf.
884940tcphttpSF220622000001000000000025350.00.00.00.01.000.000.11252551.000.000.040.040.00.00.00.0normal.
653320tcpprivateS000000000000000000011551.01.00.00.00.040.070.0025550.020.060.000.001.01.00.00.0neptune.
4906000icmpecr_iSF1032000000000000000005105100.00.00.00.01.000.000.002552551.000.001.000.000.00.00.00.0smurf.
4777340tcpprivateREJ000000000000000000224120.00.01.01.00.050.070.00255120.050.070.000.000.00.01.01.0neptune.
durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratelabel
4909550udpprivateSF280030000000000000024240.00.00.00.01.00.00.0255910.360.010.360.00.00.00.00.0teardrop.
4909560udpprivateSF280030000000000000025250.00.00.00.01.00.00.0255920.360.010.360.00.00.00.00.0teardrop.
4909570udpprivateSF280030000000000000026260.00.00.00.01.00.00.0255930.360.010.360.00.00.00.00.0teardrop.
4909580udpprivateSF280030000000000000027270.00.00.00.01.00.00.0255940.370.010.370.00.00.00.00.0teardrop.
4909590udpprivateSF280030000000000000028280.00.00.00.01.00.00.0255950.370.010.370.00.00.00.00.0teardrop.
4909600udpprivateSF280030000000000000029290.00.00.00.01.00.00.0255960.380.010.380.00.00.00.00.0teardrop.
4909610udpprivateSF280030000000000000030300.00.00.00.01.00.00.0255970.380.010.380.00.00.00.00.0teardrop.
4909620udpprivateSF280030000000000000031310.00.00.00.01.00.00.0255980.380.010.380.00.00.00.00.0teardrop.
4909630udpprivateSF280030000000000000032320.00.00.00.01.00.00.0255990.390.010.390.00.00.00.00.0teardrop.
4909640udpprivateSF280030000000000000033330.00.00.00.01.00.00.02551000.390.010.390.00.00.00.00.0teardrop.

Duplicate rows

Most frequently occurring

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratelabel# duplicates
2730icmpecr_iSF1032000000000000000005115110.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.57726
2450icmpecr_iSF520000000000000000005115110.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.10061
2710icmpecr_iSF1032000000000000000005105100.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.7876
2680icmpecr_iSF1032000000000000000005095090.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.1586
2650icmpecr_iSF1032000000000000000005085080.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.374
1830icmpecr_iSF520000000000000000004494490.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.307
2140icmpecr_iSF520000000000000000004804800.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.254
1850icmpecr_iSF520000000000000000004514510.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.246
2120icmpecr_iSF520000000000000000004784780.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.236
2160icmpecr_iSF520000000000000000004824820.00.00.00.01.00.00.02552551.00.01.00.00.00.00.00.0smurf.222